For a long time interdisciplinary perceptions have been of great importance in various fields of science and technology and have produced much more significant and far-reaching results than a more narrow point of view.

Good examples for such success can be seen in medicine, biology and in particular biotechnology. These disciplines are extensively supported by an interaction of physics, biochemistry, statistics, mathematics and more and more computer science. The continuously increasing amount of data (big data) like in genomic sequences or imaging processes require appropriate new analytic methods and techniques for their preparation.

This is the motivation and the thematic focus of the newly founded competence center for algorithmic and mathematical methods in biology, biotechnology and medicine. Combining the expertise of its members from the different faculties B, E, I and M innovative approaches to biological and medical questions should be developed, in particular with respect to problems arising in the young disciplines of systems biology and systems medicine. Here, software solutions supported by mathematical and experimental methods from biotechnology are in the main focus.

The focus of the center will initially be put on the following topics, where the center is open to other  input and members from all faculties of the University. It is our aim to improve the center's thematic orientation:

  1. Exploring the noise-immunity of the genetic code. Among other topics it will be investigated, if these properties of the genetic code can be used to improve sequencing algorithms or methods for analyzing genomes in biomedical applications. Theoretical investigations will be supported by statistical analyzes.
  2. Numerical simulation of tissues to identify mechanism for cancerogenesis. For example, simulated tissue is compared with histological sections of urothelial tissue to detect aberrations in the cell proliferation.
  3. Development of algorithms for the automated analysis of complex microscopic images. Overall aim is an enhanced understanding of population dynamics of e.g. mammalian production cells in bioreactors to improve monitoring and control of batch, fed batch and perfusion processes.
  4. Mathematical models for cancer therapy like for leucemia or the gliom.